What's breaking now, across thousands of outlets.
LLMs do not get smarter just by remembering more. EvoLib turns experience into evolving knowledge, taking reusable skills and insights that help models learn and adapt across tasks long after deployment. The post EvoLib: Turning experience into evolving knowledge appeared first on Microsoft Research .
We haven't written up this one. Microsoft Research has the full story — the link below goes straight to it.
Release: llm-chat-completions-server 0.1a0 A key goal of the new content-addressable logs in LLM 0.32rc1 was being able to support OpenAI Chat Completion style requests where each incoming message…
Release: llm 0.32rc1 This RC for LLM 0.32 finishes the work that started in LLM 0.32a0 - it adds a new schema design that does a much better job of capturing the details of the prompts and responses…
In this edition of Reporters' Notebook, our journalists discuss the ins and outs of Anthropic's Claude Mythos rollout. How seriously should we take its risks? How big of a deal is it?
Scientific hypothesis generation for AI for Science typically involves Scientific Inspiration Retrieval (SIR) followed by hypothesis composition.
More than half of businesses lack a dedicated owner for AI costs, which can lead to overspend, according to a Harness report.
SWE-bench-like benchmarks are widely used for evaluating LLM's issue resolution capability. They typically follow a common construction pipeline: each PR (Pull Request) is paired with its linked issue…
